Breakthroughs in genomic sequencing and analysis technologies are generating vast amounts of molecular feature data for both individuals and patient groups, such as a breast cancer patient group using a specific drug. In addition, the treatments used to combat various diseases and medical conditions are also rapidly expanding. The nexus of readily-available genomics and advanced medical approaches has created the opportunity to provide personalized medicine where an individual's own genetic data can be used to develop personalized treatments based on past case histories, genetic records, and medical research.
Various approaches to data structuring have been proposed to support personalized medicine based on individual genomic data. Object-oriented data, relational databases, hyper-graphs, Bayesian networks, and hierarchical temporal memories are a few examples of such approaches. Unfortunately, these approaches do not relate knowledge and data in an effective way to efficiently and robustly support personalized medicine at the molecular level.